Large language models have simplified the production of personalized translations reflecting predefined stylistic constraints. However, these systems still struggle when stylistic requirements are implicitly represented by a set of examples, such as texts produced by a specific human translator. In this work, we explore various strategies for personalizing automatically generated translations when few examples are available, with a focus on the challenging domain of literary translation. We begin by determining the feasibility of the task and how style information is encoded within model representations. Then, we evaluate various prompting strategies and inference-time interventions for steering model generations towards a personalized style, with a particular focus on contrastive steering with sparse autoencoder (SAE) latents to identify salient personalization properties. We demonstrate that contrastive SAE steering yields robust style conditioning and translation quality, resulting in higher inference-time computational efficiency than prompting approaches. We further examine the impact of steering on model activations, finding that layers encoding personalization properties are impacted similarly by prompting and SAE steering, suggesting a similar mechanism at play.

翻译：大型语言模型简化了反映预定义风格约束的个性化翻译的生成。然而，当风格要求由一组示例（例如特定人类译者产出的文本）隐式表示时，这些系统仍然面临困难。在本工作中，我们探索了在可用示例较少时对自动生成翻译进行个性化的多种策略，重点关注文学翻译这一具有挑战性的领域。我们首先评估了任务的可行性以及风格信息在模型表示中的编码方式。随后，我们评估了多种提示策略和推理时干预方法，以引导模型生成朝向个性化风格，特别关注利用稀疏自编码器（SAE）潜在变量进行对比引导，以识别显著的个人化属性。我们证明，对比性SAE引导能够产生稳健的风格控制和翻译质量，相较于提示方法，实现了更高的推理时计算效率。我们进一步研究了引导对模型激活的影响，发现编码个性化属性的层在提示和SAE引导下受到的影响相似，这表明两者可能具有相似的作用机制。